Create Trainer

First of all we need specify model, that will be trained:

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5, 1)
        self.conv2 = nn.Conv2d(20, 50, 5, 1)
        self.fc1 = nn.Linear(4 * 4 * 50, 500)
        self.fc2 = nn.Linear(500, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 4 * 4 * 50)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

Now we need build our training process. It’s done by implements Trainer class:

from neural_pipeline import FileStructManager, Trainer

# define file structure for experiment
fsm = FileStructManager(base_dir='data', is_continue=False)

# create trainer
trainer = Trainer(model, train_config, fsm, torch.device('cuda:0'))

# specify training epochs number
trainer.set_epoch_num(50)

Last parameter or Trainer constructor - target device, that will be used for training.